Evidence and Narrative in Research: From Osman & Abramson to Thermal Comfort Studies

DESN2003: Research for Innovation, Week Six

Hongshan Guo

2025-03-06

1: From Osman & Abramson to Thermal Comfort Studies: Evidence and Narrative in Research

A Data-Driven Qualitative Review in Architectural Research

2: What makes a story credible?

How do we transform raw facts into a compelling narrative?

3: Let’s open with some ideas from Osman & Abramson

  • Osman’s Idea:
    • Evidence is not self-explanatory.
    • Facts become “evidence” when selectively assembled into a narrative.
  • Abramson’s Contribution:
    • Advocates for an “undetermined history.”
    • Emphasizes embracing ambiguity, accident, and counter-narratives.

Leverage these ideas as the thread that stitches our session together.

4: Session Overview & Objectives

  • Our Focus:
    • Use Osman & Abramson as theoretical anchors to examine research narratives.
    • Case study: “A Data-Driven Qualitative Review of Thermal Comfort Studies.”
  • Learning Outcomes:
    • Understand roles of primary vs. secondary data.
    • Recognize how narrative is constructed—and what is omitted.
    • Develop strategies for critical, reflexive evidence gathering.

5: Introduction & Context Setting

  • Exploration:
    • How evidence and narrative work together in research.
    • Our case study is the provided draft on thermal comfort studies.
  • Focus:
    • Examining how the draft uses different data types to build its narrative.

6: Dissecting the Draft’s Methodology

  • Data Collection:
    • Primary Data: Field measurements, surveys, direct observations.
    • Secondary Data: Established databases (e.g., ASHRAE, Chinese TCDB) and literature.
  • Systematic Approach:
    • Literature screening, numerical evaluation criteria (scores 0–3).
    • Mapping personal (age, gender, BMI) and contextual parameters.

This method exemplifies how facts are mobilized into evidence, as Osman suggests.

-3 (cold) to 0 (neutral) to +3 (hot)

7: Mapping between perception and numbers: A Quantitative Review

What benefits and drawbacks do you see in using numerical scoring to convert raw data into a narrative?

8: Primary vs. Secondary Data Sources

  • Primary Data:
    • Direct measurements (e.g., temperature, humidity).
    • First-hand surveys and observations.
  • Secondary Data:
    • Data from previous studies and databases.
    • Provides context and broadens scope.

The draft combines both to create a layered narrative.

9: How Did We Do This in Our Manuscript?

  • Leveraging Secondary Data:
    • Utilized established thermal comfort databases (ASHRAE & Chinese TCDB) as our primary sources.
    • Compiled 88+ articles to form our evidence base.
  • Systematic Data Evaluation:
    • Applied a numerical scoring system (0–3) to assess personal, contextual, and PMV parameters.
    • Enabled structured comparison and identification of gaps across studies.
  • Constructing the Narrative:
    • Integrated diverse data points to build a coherent story that supports our hypothesis.
    • Demonstrated how selection and framing of secondary data can drive new insights.
  • Testament to Methodology:
    • Our approach shows that robust secondary data can be effectively leveraged.
    • Aligns with Osman’s and Abramson’s ideas on how evidence is reinterpreted into a narrative.

10: Critically Assessing Our Data Setup

  • Maintain Critical Awareness:
    • Triangulation: Validate findings by comparing multiple sources.
    • Reflexivity: Regularly question data selection and categorization.
    • Iterative Review: Continuously refine data collection and narrative as new evidence emerges.
  • Draft’s Limitations:
    • Inconsistent classifications and mapping issues.
    • Underrepresentation of certain demographics.
  • Future Enhancements:
    • Develop standardized classification/predictive frameworks.
    • Incorporate mixed-method approaches.

This process embodies the critical reflection championed by Osman and Abramson.

11: How can we ensure our data-driven narratives remain robust and adaptable as new evidence emerges?

12: Conclusion & Key Takeaways

  • Summary:
    • Evidence transforms into narrative through careful selection and interpretation.
    • Our case study demonstrates both the potential and limitations in this process.
  • Key Takeaways:
    • Balance rigorous data collection with reflexive narrative construction.
    • Be aware of what is included—and omitted—to strengthen research credibility.
    • Osman and Abramson remind us to remain open to alternative interpretations.

Final Reflection:
“As researchers, how do we balance the rigor of data collection with the interpretive nature of narrative construction?”

13: Thank You & Q&A

Questions & Discussion?